基于数据增强的语义一致番茄叶病识别方法

Semantic Consistent Tomato Leaf Disease Recognition Method Based on Data Augmentation

针对番茄叶片病害识别中缺乏训练图像的问题,提出一种基于数据增强的语义一致番茄叶病识别方法。首先,设计数据增强模块,对数据集进行有效扩充。然后,定义深度特征提取模块,捕捉图像中丰富的非线性语义信息。同时,为了避免数据增强过程中的语义漂移,设计语义相关最大化模块,增强原始数据和增强数据的语义相关性。最后,定义番茄叶病识别模块,实现番茄叶片病害识别。实验结果表明,相比于其他8个基线方法,本文方法在识别番茄叶片病害的准确率上达到了更优。

In response to the challenge of insufficient training images for tomato leaf disease recognition,an innovative semantic-consistent tomato leaf disease recognition method is proposed based on data augmentation.Firstly,a data augmentation module is designed to effectively expand the dataset.Next,a deep feature extraction module is defined to capture rich nonlinear semantic information from the images.To mitigate semantic drift during the data augmentation process,a semantic relevance maximization module is introduced to enhance the semantic consistency between original and augmented data.Finally,a leaf disease recognition module is defined to achieve tomato leaf disease identification.Experimental results indicate that our approach outperforms eight baseline methods in terms of accuracy.